Comparative Retrospective Analysis of ASCVD Score and Lipoprotein(a) in Predicting Outcomes of Acute Coronary Syndrome

Phillip Kim, Ji Park, Anu Poliyedath, Chinedu Nwagwu, Arianna Crediford, Shelby Wong, Courtney Hill

St. Agnes Medical Center Trinity Health - Department of Family and Internal Medicine Fresno State University - Department of Sociology and Biostatistics

BACKGROUND
Cardiovascular diseases (CVDs) remain a substantial global health burden, with prevalence nearly doubling from 271 million cases in 1990 to 523 million in 2019. (Roth, 2020) The ASCVD (Atherosclerotic Cardiovascular Disease) 10-year risk score has been widely utilized for assessing the risk of acute coronary syndrome (ACS). (Goff, 2014) However, this tool does not account for all contributory risk factors, including lipoprotein(a) [Lp(a)] which is an emerging marker implicated in predicting ACS risk. (Grundy 2019) Elevated Lp(a) levels are associated with calcific aortic valve stenosis, peripheral artery disease, ischemic stroke, and coronary atherosclerosis. (Tsao, 2023) (Goldsborough, 2022) Lp(a) has huge potential for utility however there remains no clear consensus on how to optimally incorporate Lp(a) levels into standardized risk stratification tools of ACS like ASCVD.

PURPOSE
The goal of this study is to elucidate the complex interactions Lp(a) has with ACS risk factors and demonstrate its utility when used in conjunction with ASCVD risk scoring to augment predictive power in risk stratifying patients likely to suffer from future ACS events.

METHODS
Retrospective analysis was performed. Patients were selected from Trinity Health centers. A total sample size of 1408 patients aged 40-75 years were collected. The primary end point was ACS including those who suffered ST elevation myocardial infarction (STEMI), NON-ST elevation myocardial infarction (NON-STEMI), coronary arterial bypass graft (CABG), and previous coronary stent(s) placement. Patient information included natal sex, race, age, total cholesterol, HDL cholesterol, history of smoking, diabetes, and history of hypertension treatment were collected. The population was further stratified by Lp(a) levels (according to Heart UK study guidelines) and ASCVD Risk Levels (according to 2013 ACC/AHA). Lp(a) values were reported as nmol/L from two major commercially available labs—LabCorp and Quest Diagnostic. Measuring Lp(a) in nmol/L versus milligrams per deciliter (mg/dL) provided for consistent comparisons across populations. Data analysis was completed with IBM SPSS version 29.0 including spearman rank correlation, chi-square analyses, and linear regression models.

RESULTS
Lp(a) is an emerging risk factor for atherosclerotic cardiovascular disease with growing evidence supporting its predictive value, especially in high-risk groups. (Wong 2022, Borrelli 2021) Several key challenges have impeded the development of a unified approach to Lp(a) risk assessment, including significant variability in circulating Lp(a) concentrations across different populations and ethnic groups. (Tsimikas 2017) An additional challenge is the standardization of Lp(a) assays. The gene that codes for production of lp(a), LPA, has wide variability resulting in some individuals who produce larger components within Lp(a) particle itself. This causes the ratio of particle mass compared to molecular weight to vary across individuals. Thus, the conversion of measuring units between traditional milligrams per deciliter (mg/dL) has room for error. (Alebna 2023, ACC) Whether Lp(a) should be evaluated as a separate risk factor or integrated into existing risk calculators remains a standing issue for debate (Matsuura 2019). Despite the lack of consensus and need for future studies, there is huge potential for Lp(a) to serve as a main player in screening tools for cardiovascular risk assessment in primary care and specialty care settings.

TABLES/FIGURES

Table 1
Cardiovascular risk classification conferred by Lipoprotein(a).

a Cutoffs derived from Copenhagen General Population Study [20].

Table 2
2013 10-year ASCVD risk percentage stratification.

Table 3
Breakdown summary of patient demographics

 

Table 5
ASCVD risk level of active patients.

Table 6 
Lipoprotein(a) risk level of active patients.

Table 7
Exploratory Chi-Square analysis between risk levels and ASCVD risk score variables.

Table 8
Model 1: Regression coefficients for prediction of acute coronary syndrome.

Table 9
Model 2: Regression coefficients for prediction of acute coronary syndrome.

Figure 1

Odd ratio of developing acute coronary syndrome (ACS) across different levels of the ASCVD (Atherosclerotic Cardiovascular Disease) risk impact level.

Figure 2
Odds ratio of ASCVD risk calculator categories and Lipoprotein (a) risk impact for ACS

♢ reference categorical variable; ⬤ variable  p <0.001; ⭘ categorical variable non-significance

 

DISCUSSION  
This study demonstrates the robust utility of Lp(a) in ACS risk scoring. When present in moderate or high levels Lp(a) is an additional factor that should be included in ASCVD scoring. Lp(a) can provide unique predictive value in individuals with genetic predispositions. Further studies which tease out the variability of Lp(a) across racial groups and genetic variability are needed to create a more robust screening tool.

CONCLUSION
This study demonstrates the robust utility of Lp(a) in ACS risk scoring. When present in moderate or high levels Lp(a) is an additional factor that should be included in ASCVD scoring. Lp(a) can provide unique predictive value in individuals with genetic predispositions. Further studies which tease out the variability of Lp(a) across racial groups and genetic variability are needed to create a more robust screening tool.

 

REFERENCES

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